Distribution and prevalence of Baylisascaris in domestic dogs in the United States and Canada, 2017–2023
Bibliographic record
Abstract
Dogs can serve as alternative definitive hosts for Baylisascaris procyonis , the raccoon roundworm, but prevalence and geographic distribution of canine infections is poorly studied. In a previous study in the United States (US) from 2013 to 2016, 0.005 % of ~9.5 million canine fecal samples from the United States (US) were positive for Baylisascaris . To better understand the current prevalence and distribution of Baylisascaris in dogs, fecal floatation results from commercial and academic veterinary diagnostic laboratories from the United States and Canada from 2017 to 2023 were analyzed. Baylisascaris eggs were detected in 2927 of 61,129,486 (0.0048 %) fecal samples. Positive samples originated from 47 US states, Washington D·C, and Ontario, Canada. While many positives originated in regions with high B. procyonis prevalence in raccoons, infections were also identified in several states where B. procyonis has not been reported suggesting a previously unreported presence or expanding geographic range. Higher prevalence was associated with younger dogs, large breeds, and Northeastern and Midwestern US regions. Although overall prevalence was low, Baylisascaris eggs in canine feces pose a public health risk. These eggs are highly resistant to common disinfection methods and can adhere to dog fur, increasing risk of human exposure. Given the zoonotic potential of B. procyonis , regular parasitic screening and deworming and efforts to curb coprophagy should be emphasized. Finally, given the One Health importance aspect of this parasite, public health and veterinary initiatives should continue to investigate Baylisascaris distribution in domestic and wildlife populations to better understand and mitigate risks to humans, wildlife, and exotic animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".